We are hiring an AI infrastructure expert to own the design and implementation of high-performance KV Cache systems for large-model inference, tackling the two hardest bottlenecks — memory-bound and communication-bound — to support ultra-long-context and high-concurrency serving.
We offer
Full time office work in Wola, Warsaw
Private healthcare package. We offer premium private healthcare package for our employees.
Sport Cards. Our employees can choose from many options within sport subscriptions and sport associations.
Benefit Platform. You can choose your benefits on our Benefit Platform e.g.: cinema/theater tickets and discounts, shopping cards and many more.
Special discounts for employees. We cooperate with various local companies to offer unique promotions only for our Employees.
Office massages. It is a 15-minute chair-based.
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Conduct research, design, and implementation of core KV Cache mechanisms, including intelligent eviction policies, KV compression, page-based memory management, hierarchical caching over heterogeneous memory (VRAM / host RAM / external storage), pre-scheduling and prefetching.
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Optimize KV sharding, migration and sharing for distributed inference, and resolve communication-bound bottlenecks for KV data transfer across multiple accelerators and nodes.
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Profile system performance to identify bottlenecks in memory bandwidth and communication overhead; improve end-to-end throughput via co-optimization of algorithmic policies and low-level system engineering.
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Design benchmark suites and simulation frameworks to validate KV strategy effectiveness, generate performance reports, and drive production deployment of optimized solutions.
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Collaborate closely with inference engine, scheduler, and hardware teams to deliver end-to-end optimizations for large language model inference systems.
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Solid understanding of LLM inference fundamentals and KV Cache internals. Hands-on experience building KV cache systems, general cache/storage subsystems, or high-performance networking stacks is strongly preferred.
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Proficient in performance profiling and tuning; capable of diagnosing memory, bandwidth and communication bottlenecks, with a proven track record of performance optimization on large-scale systems.
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Strong algorithmic mindset. Candidates with competitive programming backgrounds (ICPC, IMC, IOI, IMO,IOAI, etc.), first-author papers on algorithm/system performance optimization, or open-source contributions are prioritized.
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Ability to decompose complex problems independently, and drive solutions from theoretical modeling all the way through engineering implementation.
Bonus Points
- Familiar with mainstream inference frameworks including vLLM, SGLang, TensorRT-LLM, with source-code reading experience on KV Cache modules.
- Hands-on experience with heterogeneous memory, remote memory (RDMA), distributed caching, page-based storage, or custom memory pool implementations.
- Past participation in systems, storage or HPC competitions, or research experience with top-tier systems conference papers (OSDI/SOSP/ATC/NSDI, etc.).
- Familiar with multi-GPU / multi-NPU collective communication and optimization of NCCL-style collective primitives.
Education
- Master's or Ph.D. degree in Computer Science, Computer Architecture, High Performance Computing, or related fields. Exceptional bachelor candidates with strong relevant backgrounds may also be considered.
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